{
 "cells": [
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    "Frame rate and jitter analysis within ifm3d\n",
    "========================\n",
    "\n",
    "`ifm3d` provides two tools for helping to quantify expected frame rate and jitter from a given camera with a specific set of imager settings. These are `ifm3d hz` for measuring *actual* frame rate and `ifm3d jitter` for quantifying expected jitter.\n",
    "\n",
    "There are several factors that contribute to frame rate and jitter. Some are systemic like the number of frequencies, the number of exposures, imager resolution, and the requested frame rate. These, of course, need to be tempered by the overall duty cycle of the active illumination unit on the device to keep within eye safety standards. Other factors involved in *actual* frame rate and jitter are things like the computational power inside of the camera itself and the ethernet bandwidth of both the camera and recieving computer. Finally, factors like the PCIC schema used (i.e., which image types we are transmitting from the camera back to `ifm3d`) *and* the type of image buffer used within `ifm3d` could also play a role and are not independent of the aforementioned items.\n",
    "\n",
    "In this document, we will use a concrete example of specific imager settings across two different ifm cameras, the O3D and the O3X. We will use the tools supplied with `ifm3d` to conduct an analysis on both frame rate and jitter with these particular imager settings. We need to be clear, the imager settings used in this document are for exemplary purposes only and to help us draw some conclusions to get a better understanding of our expected frame rate and jitter when using `ifm3d` and the O3D or the O3X. We do not recommend the particular imager settings used in this document as suitable for any particular application. These are for exemplary purposes only. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tools\n",
    "-----------\n",
    "\n",
    "Before we get started, let's get acquainted with the tools. The first tool we will use in this document is `ifm3d hz`. It is used to get a sense of the actual frame rate from a running camera. Its help output is as follows:\n",
    "\n",
    "```\n",
    "$ ifm3d hz --help\n",
    "usage: ifm3d [<global options>] hz [<hz options>]\n",
    "\n",
    "global options:\n",
    "  -h [ --help ]            Produce this help message and exit\n",
    "  --ip arg (=192.168.0.69) IP address of the sensor\n",
    "  --xmlrpc-port arg (=80)  XMLRPC port of the sensor\n",
    "  --password arg           Password for establishing an edit-session with the \n",
    "                           sensor\n",
    "\n",
    "hz options:\n",
    "  --nframes arg (=10)   Number of frames to capture\n",
    "  --nruns arg (=1)      Number of runs to compute summary statistics over\n",
    "  --sw                  Software Trigger the FrameGrabber\n",
    "\n",
    "```\n",
    "\n",
    "The second tool we will use is `ifm3d jitter`. This tool allows us to perform a jitter analysis from a running camera across whichever supported `ifm3d` image containers you have compiled into your binary. The `ifm3d jitter` help output is as follows:\n",
    "\n",
    "```\n",
    " $ ifm3d jitter --help\n",
    "usage: ifm3d [<global options>] jitter [<jitter options>]\n",
    "\n",
    "global options:\n",
    "  -h [ --help ]            Produce this help message and exit\n",
    "  --ip arg (=192.168.0.69) IP address of the sensor\n",
    "  --xmlrpc-port arg (=80)  XMLRPC port of the sensor\n",
    "  --password arg           Password for establishing an edit-session with the \n",
    "                           sensor\n",
    "\n",
    "jitter options:\n",
    "  --nframes arg (=100)  Number of frames to capture\n",
    "  --outfile arg (=-)    Raw data output file, if not specified, nothing is \n",
    "                        written\n",
    "```\n",
    "\n",
    "As noted above, `ifm3d jitter` computes the jitter metrics for each image container you have built into your binaries. For purposes of this document, our `cmake` build line looked like `cmake -DCMAKE_INSTALL_PREFIX=/usr -DBUILD_MODULE_OPENCV=ON ..` So, in our `ifm3d jitter` output, we will gather statistics for the `ifm3d::ByteBuffer`, `ifm3d::ImageBuffer`, and `ifm3d::OpenCVBuffer`. Let's quickly discuss what this means so we can properly interpret our data. The `ifm3d::ByteBuffer` is the baseline container used in the `ifm3d` framegrabber module. Statistics related to the `ifm3d::ByteBuffer` gives us metrics on acquiring data from the camera, packetizing it locally, and signaling our control thread that data are ready. However, no parsing of the pixel bytes or image container construction is done. In theory, this should be the *fastest* albeit the most useless. The `ifm3d::ImageBuffer` constructs images prior to signaling the control thread. In the event the PCIC schema specified the Cartesian Data (i.e., the point cloud to be computed on the camera and transmitted over the wire to `ifm3d`) the `ifm3d::ImageBuffer` will create point cloud data structures as both a PCL point cloud and an OpenCV image (3 channels, one for each of the x, y, z spatial planes). Finally, the `ifm3d::OpenCVBuffer` is very similar to `ifm3d::ImageBuffer` except it does not create PCL bindings, only OpenCV. So, to be clear, depending upon the image container used and the PCIC schema specified, different amounts of work is done by the `ifm3d` library. We should keep this in mind when looking at our frame rate and jitter statistics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PCIC schemas used\n",
    "------------------------\n",
    "\n",
    "A PCIC schema specifies which image types should be computed by the camera and transmitted back to `ifm3d` for processing. In this document, we use two schemas for exemplary purposes. The first is `10`, which looks like:\n",
    "\n",
    "```\n",
    "$ ifm3d schema --mask=10\n",
    "mask=10, str=-\n",
    "---\n",
    "PCIC (O3D-compatible): \n",
    "\n",
    "      {\n",
    "        \"layouter\": \"flexible\",\n",
    "        \"format\"  : {\"dataencoding\":\"ascii\"},\n",
    "        \"elements\":\n",
    "         [\n",
    "           {\"type\":\"string\", \"value\":\"star\", \"id\":\"start_string\"},\n",
    "           {\"type\":\"blob\", \"id\":\"normalized_amplitude_image\"},\n",
    "           {\"type\":\"blob\", \"id\":\"x_image\"},\n",
    "           {\"type\":\"blob\", \"id\":\"y_image\"},\n",
    "           {\"type\":\"blob\", \"id\":\"z_image\"},\n",
    "           {\"type\":\"blob\", \"id\":\"confidence_image\"},\n",
    "           {\"type\":\"blob\", \"id\":\"extrinsic_calibration\"},\n",
    "           {\"type\":\"string\", \"value\":\"stop\", \"id\":\"end_string\"}\n",
    "         ]\n",
    "      }\n",
    "   \n",
    "---\n",
    "XML-RPC (O3X-compatible): \n",
    "\n",
    "      {\n",
    "         \"Apps\":\n",
    "         [\n",
    "           {\n",
    "             \"Index\":\"1\",\n",
    "             \"OutputDistanceImage\":\"false\",\n",
    "             \"OutputAmplitudeImage\":\"true\",\n",
    "             \"OutputGrayscaleImage\":\"false\",\n",
    "             \"OutputXYZImage\":\"true\",\n",
    "             \"OutputConfidenceImage\":\"true\"\n",
    "            }\n",
    "         ]\n",
    "      }\n",
    "\n",
    "```\n",
    "\n",
    "As shown above the `10` mask gives us the normalized amplitude and Cartesian data (point cloud computed by the camera and transmitted over the wire back to `ifm3d`). Additionally, the confidence and extrinsic calibration (o3d) data is transmitted as `ifm3d` enforces this as an invariant.\n",
    "\n",
    "The second schema we use is `3`, which looks like:\n",
    "\n",
    "```\n",
    "$ ifm3d schema --mask=3\n",
    "mask=3, str=-\n",
    "---\n",
    "PCIC (O3D-compatible): \n",
    "\n",
    "      {\n",
    "        \"layouter\": \"flexible\",\n",
    "        \"format\"  : {\"dataencoding\":\"ascii\"},\n",
    "        \"elements\":\n",
    "         [\n",
    "           {\"type\":\"string\", \"value\":\"star\", \"id\":\"start_string\"},\n",
    "           {\"type\":\"blob\", \"id\":\"distance_image\"},\n",
    "           {\"type\":\"blob\", \"id\":\"normalized_amplitude_image\"},\n",
    "           {\"type\":\"blob\", \"id\":\"confidence_image\"},\n",
    "           {\"type\":\"blob\", \"id\":\"extrinsic_calibration\"},\n",
    "           {\"type\":\"string\", \"value\":\"stop\", \"id\":\"end_string\"}\n",
    "         ]\n",
    "      }\n",
    "   \n",
    "---\n",
    "XML-RPC (O3X-compatible): \n",
    "\n",
    "      {\n",
    "         \"Apps\":\n",
    "         [\n",
    "           {\n",
    "             \"Index\":\"1\",\n",
    "             \"OutputDistanceImage\":\"true\",\n",
    "             \"OutputAmplitudeImage\":\"true\",\n",
    "             \"OutputGrayscaleImage\":\"false\",\n",
    "             \"OutputXYZImage\":\"false\",\n",
    "             \"OutputConfidenceImage\":\"true\"\n",
    "            }\n",
    "         ]\n",
    "      }\n",
    "```\n",
    "\n",
    "As is shown above, in this case, we return the invariants and just the normalized amplitude and radial distance image. Assuming we have access to the unit vectors, such a schema would support point cloud computation off-board the camera.\n",
    "\n",
    "For purposes of this document, the selection of these two schemas allows us to reason about frame rate and jitter as a function of camera-side computation, data transmission over the wire, and pixel/byte-parsing PC-side (i.e., in `ifm3d`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The imager settings used\n",
    "----------------------------\n",
    "\n",
    "The pertinent imager settings we contemplate in this document across both the O3D and O3X include a frame rate of `10` Hz, an imager type of `upto30m_moderate` (which means, two frequencies and two exposures), a long exposure time of `550` usecs, and an exposure time ratio of `50`. We also note that for our O3D tests, we use the standard 23k pixel imager and for the O3X the 38k pixel imager. Full json outputs from running `ifm3d dump` for both the O3D and O3X are made available in the same documentation directory as this Jupyter notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O3D Analysis\n",
    "=======\n",
    "\n",
    "We start our analysis with the O3D. Let's first get a sense of actual frame rate for both the `10` and `3` schemas:\n",
    "\n",
    "```\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=10 ifm3d hz --nframes=100\n",
    "FrameGrabber running at: 10.0353 Hz\n",
    "100 frames captured, over 1 runs\n",
    "\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=3 ifm3d hz --nframes=100\n",
    "FrameGrabber running at: 10.0101 Hz\n",
    "100 frames captured, over 1 runs\n",
    "```\n",
    "\n",
    "We can see that with the above mentioned imager settings, the O3D can deliver the data and `ifm3d` can process it at 10 Hz. Let's look at the jitter across each image container for each schema.\n",
    "\n",
    "```\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=10 ifm3d jitter --outfile=o3d_10.csv\n",
    "Capturing frame data for ifm3d::ByteBuffer...\n",
    "Mean:   99.44 ms\n",
    "Median: 99.2376 ms\n",
    "Stdev:  4.16 ms\n",
    "Mad:    3.73891 ms\n",
    "\n",
    "Capturing frame data for ifm3d::ImageBuffer...\n",
    "Mean:   99.45 ms\n",
    "Median: 99.3525 ms\n",
    "Stdev:  4.48934 ms\n",
    "Mad:    4.01406 ms\n",
    "\n",
    "Capturing frame data for ifm3d::OpenCVBuffer...\n",
    "Mean:   99.4 ms\n",
    "Median: 99.3184 ms\n",
    "Stdev:  4.27812 ms\n",
    "Mad:    3.78261 ms\n",
    "Raw data has been written to: o3d_10.csv\n",
    "\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=3 ifm3d jitter --outfile=o3d_3.csv\n",
    "Capturing frame data for ifm3d::ByteBuffer...\n",
    "Mean:   99.41 ms\n",
    "Median: 102.144 ms\n",
    "Stdev:  4.86979 ms\n",
    "Mad:    3.23146 ms\n",
    "\n",
    "Capturing frame data for ifm3d::ImageBuffer...\n",
    "Mean:   99.53 ms\n",
    "Median: 102.025 ms\n",
    "Stdev:  4.65156 ms\n",
    "Mad:    2.56628 ms\n",
    "\n",
    "Capturing frame data for ifm3d::OpenCVBuffer...\n",
    "Mean:   99.47 ms\n",
    "Median: 102.323 ms\n",
    "Stdev:  4.82573 ms\n",
    "Mad:    2.58829 ms\n",
    "Raw data has been written to: o3d_3.csv\n",
    "```\n",
    "\n",
    "We can see that `ifm3d jitter` outputs the *mean*, *median*, *standard deviation*, and the *median absolute error (MAD)*. For purposes of this document, we will focus on the *median* and the *mad*. Justification for looking at these statistics specifically, is best described in [this whitepaper](http://zeromq.org/whitepapers:measuring-jitter). To this end, for exemplary purposes, let's interpret the meaning of this output for the `3` schema and the `ifm3d::OpenCVBuffer` image container. We can say, *\"the most typical deviation from the most typical latency of 102.323 ms will be 2.58829 ms\"*.\n",
    "\n",
    "In our above command lines, we also asked `ifm3d jitter` to ouput the raw data so we can visualize it. Let's do that now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def plot_raw(infile, title=''):\n",
    "    df = pd.read_csv(infile, sep=',', header=0)\n",
    "    ax = df.plot(marker='.', linestyle='', grid=True, title=title)\n",
    "    ax.set_xlabel(\"Sample Number\")\n",
    "    ax.set_ylabel(\"Latency (ms)\")\n",
    "    return df\n",
    "\n",
    "def plot_pct(df, title):\n",
    "    df2 = df.quantile(q=np.linspace(0,1,df.shape[0]))\n",
    "    ax = df2.plot(marker='x', linestyle='', grid=True, title=title)\n",
    "    ax.set_xlabel(\"% of sample\")\n",
    "    ax.set_ylabel(\"Latency (ms)\")\n",
    "    return df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36506edda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3d10_df = plot_raw('o3d_10.csv', title=\"O3D - 10 schema\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36478e1c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3d10_dfq = plot_pct(o3d10_df, title=\"O3D-10 Quantile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36477d24e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3d3_df = plot_raw('o3d_3.csv', title=\"O3D - 3 schema\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36478f53c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3d3_dfq = plot_pct(o3d3_df, title=\"O3D-3 Quantile\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have all of our raw data plotted for both the `10` and `3` schemas as well as each of our available image containers, what can we say about this imager setting running on the O3D that the summary statistics to not reveal? First, we can see that there is certainly noticeable jitter and a clear separation around 100 ms of latency. These non-smooth curves seem to indicate that while the camera can deliver data at this rate and `ifm3d` can construct data containers for us, the imager setting is not well-behaved.\n",
    "\n",
    "Additionally, to be clear on the quantile plots, the information we are conveying is the answer to the question: What is the worst latency to expect in N% of the cases? To do that, you look along the horizontal axis to pick N, then go up the vertical axis to get the answer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O3X Analysis\n",
    "=======\n",
    "\n",
    "We now use the same imager settings as above from the O3D, but on the O3X.\n",
    "\n",
    "```\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=10 ifm3d hz --nframes=100\n",
    "FrameGrabber running at: 6.53846 Hz\n",
    "100 frames captured, over 1 runs\n",
    "\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=3 ifm3d hz --nframes=100\n",
    "FrameGrabber running at: 9.33747 Hz\n",
    "100 frames captured, over 1 runs\n",
    "```\n",
    "\n",
    "The first thing that jumps out at us is that we cannot achieve the requested 10 Hz as we did with the O3D and the frame rate is sensitive to the PCIC schema mask (where the point cloud is constructed). Now, the problem can be with `ifm3d` or with the image computation on the O3X itself. We keep `ifm3d` as a candidate for the problem here as in this experiment we have the O3X delivering images at 38k pixels while the O3D is only at 23k pixels. Could parsing these extra pixels actually be the cause of our inability to achieve 10 Hz? Let's keep digging. But first some jitter analysis.\n",
    "\n",
    "```\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=10 ifm3d jitter --outfile=o3x_10_dualfreq.csv\n",
    "Capturing frame data for ifm3d::ByteBuffer...\n",
    "Mean:   168.34 ms\n",
    "Median: 155.969 ms\n",
    "Stdev:  48.3401 ms\n",
    "Mad:    6.72388 ms\n",
    "\n",
    "Capturing frame data for ifm3d::ImageBuffer...\n",
    "Mean:   131.96 ms\n",
    "Median: 109.219 ms\n",
    "Stdev:  78.9582 ms\n",
    "Mad:    4.81717 ms\n",
    "\n",
    "Capturing frame data for ifm3d::OpenCVBuffer...\n",
    "Mean:   153.98 ms\n",
    "Median: 146.862 ms\n",
    "Stdev:  54.0374 ms\n",
    "Mad:    10.6415 ms\n",
    "Raw data has been written to: o3x_10_dualfreq.csv\n",
    "\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=3 ifm3d jitter --outfile=o3x_3_dualfreq.csv\n",
    "IFM3D_MASK=3 ifm3d jitter --outfile=o3x_3_dualfreq.csv\n",
    "Capturing frame data for ifm3d::ByteBuffer...\n",
    "Mean:   102.57 ms\n",
    "Median: 103.009 ms\n",
    "Stdev:  0.857179 ms\n",
    "Mad:    0.216187 ms\n",
    "\n",
    "Capturing frame data for ifm3d::ImageBuffer...\n",
    "Mean:   102.55 ms\n",
    "Median: 102.98 ms\n",
    "Stdev:  0.910114 ms\n",
    "Mad:    0.277157 ms\n",
    "\n",
    "Capturing frame data for ifm3d::OpenCVBuffer...\n",
    "Mean:   102.37 ms\n",
    "Median: 102.942 ms\n",
    "Stdev:  0.946565 ms\n",
    "Mad:    0.196693 ms\n",
    "Raw data has been written to: o3x_3_dualfreq.csv\n",
    "```\n",
    "\n",
    "As we can see, there is a tremendous amount of jitter with the `10` schema. Using the same example from above, if we look at the `3` schema, where the jitter appears acceptable, and the `ifm3d::OpenCVBuffer`, we can say *\"the most typical deviation from the most typical latency of 102.942 ms will be .196693 ms\"*. As we saw from above, looking at the raw data may reveal additional data patterns not made clear in the summary statistics. Let's look.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36478db9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3x10_dual_df = plot_raw('o3x_10_dualfreq.csv', title=\"O3X Dual Frequency - 10 schema\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36477e3e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3x10_dual_dfq = plot_pct(o3x10_dual_df, title=\"O3X Dual Frequency - 10 Quantile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36477a9da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3x3_dual_df = plot_raw('o3x_3_dualfreq.csv', title=\"O3X Dual Frequency - 3 schema\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3647777fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3x3_dual_dfq = plot_pct(o3x3_dual_df, title=\"O3X Dual Frequency - 3 Quantile\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What conclusions can be drawn from these data? It is the opinion of this author, that it is not entirely clear, however it seems that when we offload the Cartesian computation away from the O3X camera, the results are more stable. Let's look at another configuration on the O3X to further test this hypothesis. Specifically, we will keep all of the imager settings the same with the exception of running in a single frequency dual exposure mode instead of dual frequency dual exposure. This significantly cuts down the phase data processing on the O3X camera however, from the `ifm3d` perspective, the same amount of data need to be parsed and constructed into our image containers. To be clear, let's lessen the work on the camera, but keep the data processing load on the PC (`ifm3d`) side consistent.\n",
    "\n",
    "```\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=10 ifm3d hz --nframes=100\n",
    "FrameGrabber running at: 9.59558 Hz\n",
    "100 frames captured, over 1 runs\n",
    "\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=3 ifm3d hz --nframes=100\n",
    "FrameGrabber running at: 9.53828 Hz\n",
    "100 frames captured, over 1 runs\n",
    "```\n",
    "\n",
    "The first thing we notice is that we can get get the data at 10 Hz now regardless of the schema, which we could not do before in dual frequency mode. Let's compute the jitter statistics.\n",
    "\n",
    "```\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=10 ifm3d jitter --outfile=o3x_10_singlefreq.csv\n",
    "Capturing frame data for ifm3d::ByteBuffer...\n",
    "Mean:   102.58 ms\n",
    "Median: 103.038 ms\n",
    "Stdev:  0.982037 ms\n",
    "Mad:    0.395645 ms\n",
    "\n",
    "Capturing frame data for ifm3d::ImageBuffer...\n",
    "Mean:   102.43 ms\n",
    "Median: 102.992 ms\n",
    "Stdev:  0.93556 ms\n",
    "Mad:    0.314056 ms\n",
    "\n",
    "Capturing frame data for ifm3d::OpenCVBuffer...\n",
    "Mean:   102.44 ms\n",
    "Median: 103.001 ms\n",
    "Stdev:  1.10754 ms\n",
    "Mad:    0.292614 ms\n",
    "Raw data has been written to: o3x_10_singlefreq.csv\n",
    "\n",
    "[ ~/dev/ifm3d/doc/jitter ]\n",
    "uspanzto@uspanzto20: $ IFM3D_MASK=3 ifm3d jitter --outfile=o3x_3_singlefreq.csv\n",
    "Capturing frame data for ifm3d::ByteBuffer...\n",
    "Mean:   102.47 ms\n",
    "Median: 102.981 ms\n",
    "Stdev:  0.741778 ms\n",
    "Mad:    0.126064 ms\n",
    "\n",
    "Capturing frame data for ifm3d::ImageBuffer...\n",
    "Mean:   102.42 ms\n",
    "Median: 102.998 ms\n",
    "Stdev:  0.997416 ms\n",
    "Mad:    0.112106 ms\n",
    "\n",
    "Capturing frame data for ifm3d::OpenCVBuffer...\n",
    "Mean:   102.4 ms\n",
    "Median: 102.991 ms\n",
    "Stdev:  1.06245 ms\n",
    "Mad:    0.196171 ms\n",
    "Raw data has been written to: o3x_3_singlefreq.csv\n",
    "```\n",
    "\n",
    "We can see that these jitter numbers appear much more stable. To be consistent with above, we can see that for the `3` schema and the `ifm3d::OpenCVBuffer` *\"the most typical deviation from the most typical latency of 102.991 ms will be .196171 ms\"*.\n",
    "\n",
    "Let's look at the raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36476175c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3x10_single_df = plot_raw('o3x_10_singlefreq.csv', title=\"O3X Single Frequency - 10 schema\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36475afe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3x10_single_dfq = plot_pct(o3x10_single_df, title=\"O3X Single Frequency - 10 Quantile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36475325c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3x3_single_df = plot_raw('o3x_3_singlefreq.csv', title=\"O3X Single Frequency - 3 schema\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36474f9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o3x3_single_dfq = plot_pct(o3x3_single_df, title=\"O3X Single Frequency - 3 Quantile\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These single frequency data on the O3X seem much more inline with a stable imager setting (regardless of PCIC schema) than we saw above with the O3X or O3D in dual frequency mode with these particular imager settings. Further conclusions are left as an exercise for the reader.\n",
    "\n",
    "We must state again that the imager parameters used here for exemplary purposes are not endorsed in any way by ifm or the authors of this document as suitable for any particular application. They were simply used to help the reader experiment with the analytic tooling provided natively by `ifm3d`. Additionally, we remind the reader that all code and data used in this analysis are available in the `ifm3d` source repository."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
